Currently many efficient object recognition technologies are available which are based on use of data on object element image distance (hereinafter—z-buffer) from a specific sensor.
The most popular z-buffering devices are radars, including laser type, and various sensors. Examples of these systems are described in Russian Patent No. 2092869 “Motor vehicle traffic safety radar”, Russian Patent No. 113398 “Motor vehicle speed and coordinate system”, Russian Patent No. 112118 “Pedestrian protection from collision with a vehicle”, Russian Patent No. 96951 “Radar-Navigator system”, Russian Patent No. 2452033 “Night vision systems and methods”, Russian Patent No. 2449375 “Motor vehicle preview capacity detector”.
All of the above systems are used in active technologies, i.e. requiring mandatory exposure of the analyzed object to illumination. The major drawbacks of such technical solutions include: background light sensitivity (bright sun etc.)—functionality reduces depending on flash intensity; sensitivity to operation of a similar system nearby (interference)—the system detects flashing from such other system and reduces its own functionality.
Stereo-based z-buffering is another method of object recognition. In this case z-buffer is built on the basis of two or more synchronized object images. Z-buffer allows object stratification by depth yet autonomously it is incapable of predicting their motion, since it requires object speed data, which can be computed with the aid of optical image sequence processing.
The most congenial technical solution, or prototype, is represented by the approach described in the article “Dense, Robust, and Accurate Motion Field Estimation from Stereo Images sequences in Real-time” by Rabe et al. This system allows object motion estimation on the basis of data transmitted from camera stereo. The system is composed of the following modules: cameras (a), image decoder (b), rectifier (c), disparity computer (object image interval in the first and second images of the rectified stereo) (d), image point motion computer (e), noise reducer (f), controller (g). This approach was developed into video adapter software integration.
The limitations of this system consists in the fact that object detection and distance estimation are based on comparison of all shots and its inability to provide comparison of all the shots, high computation requirements and incompatibility with only video adapter based PLO on-line integration. Not all of the algorithms used are capable of providing a high paralleling capacity, which results in limited system acceleration and complicating the development of a compact on-line device.